3D Scene Modeling and Rendering Algorithm Based on Road Extraction of Simple Attribute Remote Sensing Image

Zhang, Haiying; Lü, Qiang; Lian, Yuanfeng

doi:10.2991/cmsa-18.2018.4

Cited by 2 publications

(5 citation statements)

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“…In the given 3D scenes, GaLin et al [9] discretized the shortest path created by them to generate piecewise clothoid, and then combined with terrain and obstacles so that the roads with slope are created. Wang et al [26] extracted road center axes from simple attribute remote sensing images, and then combined them with terrain to create 3D road network. The roads in the above two scenes both had the width and textures, but did not support the generation and display of intersections.…”

Section: D Scene Modelingmentioning

confidence: 99%

“…There are two kinds of scenes. The scenes in [3,9] are created by virtual simulation, the other scenes [7,26] are generated based on the real data completely and we classified it as realistic reconstruction. Galin [9] and Wang [26] supported for generating neither intersections nor traffic data.…”

Section: Comparison Studymentioning

confidence: 99%

“…The scenes in [3,9] are created by virtual simulation, the other scenes [7,26] are generated based on the real data completely and we classified it as realistic reconstruction. Galin [9] and Wang [26] supported for generating neither intersections nor traffic data. Brunton [3] could generate simple trigeminal intersection and the four corners, but they did not mention complex intersections.…”

Section: Comparison Studymentioning

confidence: 99%

“…Cura [7] also generates intersections by combining the crossing angle of roads, and this method is suitable for intersections with various Figure 8: The first row shows the 3D road surfaces generated by the method of texture mapping based projection using ArcGIS. The second row figures are generated by Wang [26]. The rest of these fs are generated by our method.…”

Section: Generation Of Intersectionmentioning

confidence: 99%

“…However, there are still lots of difficulties for data denoising and texture reconstruction. The other method combines common GIS data to generate 3D roads [9,26]. The satellite imagery is used as texture of roads.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Automatic Generation of Large-scale 3D Road Networks based on GIS Data

Wang¹,

Wu²,

Han³

et al. 2021

Preprint

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How to automatically generate a realistic large-scale 3D road network is a key point for immersive and credible traffic simulations. Existing methods cannot automatically generate various kinds of intersections in 3D space based on GIS data. In this paper, we propose a method to generate complex and large-scale 3D road networks automatically with the open source GIS data, including satellite imagery, elevation data and two-dimensional(2D) road center axis data, as input. We first introduce a semantic structure of road network to obtain high-detailed and well-formed networks in a 3D scene. We then generate 2D shapes and topological data of the road network according to the semantic structure and 2D road center axis data. At last, we segment the elevation data and generate the surface of the 3D road network according to the 2D semantic data and satellite imagery data. Results show that our method does well in the generation of various types of intersections and the high-detailed features of roads. The traffic semantic structure, which must be provided in traffic simulation, can also be generated automatically according to our method.

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Section: D Scene Modelingmentioning

confidence: 99%

Section: Comparison Studymentioning

confidence: 99%

Section: Comparison Studymentioning

confidence: 99%

Section: Generation Of Intersectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic Generation of Large-scale 3D Road Networks based on GIS Data

Wang¹,

Wu²,

Han³

et al. 2021

Preprint

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Novel Framework for 3D Road Extraction Based on Airborne LiDAR and High-Resolution Remote Sensing Imagery

et al. 2021

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3D GIS has attracted increasing attention from academics, industries, and governments with the increase in the requirements for the interoperability and integration of different sources of spatial data. Three-dimensional road extraction based on multisource remote sensing data is still a challenging task due to road occlusion and topological complexity. This paper presents a novel framework for 3D road extraction by integrating LiDAR point clouds and high-resolution remote sensing imagery. First, a multiscale collaborative representation-based road probability estimation method was proposed to segment road surfaces from high-resolution remote sensing imagery. Then, an automatic stratification process was conducted to specify the layer values of each road segment. Additionally, a multifactor filtering strategy was proposed in consideration of the complexity of ground features and the existence of noise in LiDAR points. Lastly, a least-square-based elevation interpolation method is used for restoring the elevation information of road sections blocked by overpasses. The experimental results based on two datasets in Hong Kong Island show that the proposed method obtains competitively satisfactory results.

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3D Scene Modeling and Rendering Algorithm Based on Road Extraction of Simple Attribute Remote Sensing Image

Cited by 2 publications

References 0 publications

Automatic Generation of Large-scale 3D Road Networks based on GIS Data

Automatic Generation of Large-scale 3D Road Networks based on GIS Data

Novel Framework for 3D Road Extraction Based on Airborne LiDAR and High-Resolution Remote Sensing Imagery

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